article

An improved face recognition technique based on modular PCA approach

Authors:

Rajkiran Gottumukkal,

Vijayan K. AsariAuthors Info & Claims

Pattern Recognition Letters, Volume 25, Issue 4

Pages 429 - 436

https://doi.org/10.1016/j.patrec.2003.11.005

Published: 01 March 2004 Publication History

Abstract

A face recognition algorithm based on modular PCA approach is presented in this paper. The proposed algorithm when compared with conventional PCA algorithm has an improved recognition rate for face images with large variations in lighting direction and facial expression. In the proposed technique, the face images are divided into smaller sub-images and the PCA approach is applied to each of these sub-images. Since some of the local facial features of an individual do not vary even when the pose, lighting direction and facial expression vary, we expect the proposed method to be able to cope with these variations. The accuracy of the conventional PCA method and modular PCA method are evaluated under the conditions of varying expression, illumination and pose using standard face databases.

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An improved face recognition technique based on modular PCA approach

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Published In

Pattern Recognition Letters Volume 25, Issue 4

March 2004

140 pages

ISSN:0167-8655

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Elsevier Science Inc.

United States

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Published: 01 March 2004

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Lyu Y(2024)An Improved Image Recognition Technique Based on SPCA-GA-SVM: Dimensionality Reduction and ClassificationProceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms10.1145/3690407.3690572(998-1002)Online publication date: 21-Jun-2024
https://dl.acm.org/doi/10.1145/3690407.3690572
Imran AAhmed RHasan MAhmed MAzad AAlyami S(2024)FaceEngine: A Tracking-Based Framework for Real-Time Face Recognition in Video Surveillance SystemSN Computer Science10.1007/s42979-024-02922-15:5Online publication date: 31-May-2024
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Zhang WVenkata Durga Kumar BJ.A Ooi KYew Teh JSalam IArif Sobhan Bhuiyan M(2022)An Analysis on Face Recognition using Principal Component Analysis ApproachProceedings of the 2022 5th International Conference on Image and Graphics Processing10.1145/3512388.3512411(150-158)Online publication date: 7-Jan-2022
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Zhang ZZhang LZhang M(2021)Dissimilarity-based nearest neighbor classifier for single-sample face recognitionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-020-01827-337:4(673-684)Online publication date: 1-Apr-2021
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Wang ZLi X(2020)Eco-tourism benefit evaluation of Yellow River based on principal component analysisJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-18928839:6(8907-8915)Online publication date: 1-Jan-2020
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Zhang YXiao XYang LXiang YZhong S(2020)Secure and Efficient Outsourcing of PCA-Based Face RecognitionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2019.294787215(1683-1695)Online publication date: 1-Jan-2020
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Sahoo TBanka HNegi A(2020)Novel approaches to one-directional two-dimensional principal component analysis in hybrid pattern frameworkNeural Computing and Applications10.1007/s00521-018-3892-432:9(4897-4918)Online publication date: 1-May-2020
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Mi JZhu QLu J(2019)Principal component analysis based on block-norm minimizationApplied Intelligence10.1007/s10489-018-1382-049:6(2169-2177)Online publication date: 1-Jun-2019
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Chu YZhao LAhmad T(2019)Multiple feature subspaces analysis for single sample per person face recognitionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-017-1468-435:2(239-256)Online publication date: 1-Feb-2019
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Zeng JZhao XGan JMai CZhai YWang F(2018)Deep Convolutional Neural Network Used in Single Sample per Person Face RecognitionComputational Intelligence and Neuroscience10.1155/2018/38036272018Online publication date: 23-Aug-2018
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